Summary Statistics

Species Information

##           y      ymin      ymax
## 1 0.4225771 0.3627116 0.4824426

Species tables and tree tables

## Parsed with column specification:
## cols(
##   Species = col_character(),
##   `Scientific Name` = col_character(),
##   `Func. Group` = col_character(),
##   Sightings = col_double(),
##   Ingestions = col_double(),
##   Removals = col_double(),
##   Nibbles = col_double(),
##   `Avg. Vistitation Rate` = col_double(),
##   `Avg. Fruit Removal Rate` = col_double(),
##   SDE = col_double(),
##   Class = col_character()
## )
##  [1] "Species"                 "Scientific Name"        
##  [3] "Func. Group"             "Sightings"              
##  [5] "Ingestions"              "Removals"               
##  [7] "Nibbles"                 "Avg. Vistitation Rate"  
##  [9] "Avg. Fruit Removal Rate" "SDE"                    
## [11] "Class"

## # A tibble: 2 x 4
##   Height count   mean     sd
##   <fct>  <int>  <dbl>  <dbl>
## 1 high      25 0.301  0.387 
## 2 low       45 0.0516 0.0580
##          TH Tree Height     visits fruit.rem.rate         SDE
## 1   258_low  258    low 0.36210317    0.150000000 0.054315476
## 2  258_high  258   high 1.01686508    0.524450549 0.533295450
## 3    13_low   13    low 1.25000000    0.153005464 0.191256831
## 4   13_high   13   high 0.77380952    0.634146341 0.490708479
## 5    18_low   18    low 0.20568783    0.006410256 0.001318512
## 6    79_low   79    low 0.76315438    0.153846154 0.117408366
## 7   79_high   79   high 2.00983045    0.559900109 1.125304287
## 8  250_high  250   high 0.00000000    0.000000000 0.000000000
## 9  388_high  388   high 1.59523810    0.806991774 1.287344021
## 10  388_low  388    low 0.51785714    0.054347826 0.028144410
## 11  406_low  406    low 1.31944444    0.098039216 0.129357298
## 12 406_high  406   high 0.62500000    0.571428571 0.357142857
## 13  200_low  200    low 1.51515152    0.115942029 0.175669741
## 14  203_low  203    low 2.17532468    0.066798523 0.145308476
## 15   6_high    6   high 0.19439935    0.702077922 0.136483492
## 16    6_low    6    low 0.11842324    0.080536312 0.009537371
## 17   75_low   75    low 0.15625000    0.000000000 0.000000000
## 18 203_high  203   high 0.25595238    0.731884058 0.187327467
## 19  90_high   90   high 0.42981902    0.561310976 0.241262135
## 20  205_low  205    low 0.24122807    0.136363636 0.032894737
## 21  25_high   25   high 0.03472222    1.000000000 0.034722222
## 22   41_low   41    low 0.00000000    0.000000000 0.000000000
## 23  41_high   41   high 0.28645833    0.714285714 0.204613095
## 24   92_low   92    low 0.28905508    0.070530733 0.020387267
## 25  67_high   67   high 0.37500000    1.000000000 0.375000000
## 26  262_low  262    low 0.06410256    0.000000000 0.000000000
## 27 293_high  293   high 0.00000000    0.000000000 0.000000000
## 28    8_low    8    low 0.07575758    0.000000000 0.000000000
## 29   19_low   19    low 0.00000000    0.000000000 0.000000000
## 30  144_low  144    low 0.64406318    0.177388836 0.114249618
## 31 160_high  160   high 0.00000000    0.000000000 0.000000000
## 32   90_low   90    low 0.09572218    0.190476190 0.018232796
## 33 138_high  138   high 0.35054200    0.456794294 0.160125586
## 34  138_low  138    low 0.32778922    0.258405694 0.084702600
## 35  127_low  127    low 0.07502914    0.104761905 0.007860195
## 36 127_high  127   high 0.06944444    0.750000000 0.052083333
## 37   60_low   60    low 0.00000000    0.000000000 0.000000000
## 38  82_high   82   high 1.64368964    0.769627660 1.265029014
## 39   82_low   82    low 0.72448385    0.253885148 0.183935689
## 40  107_low  107    low 0.06578947    0.666666667 0.043859649
## 41  121_low  121    low 0.05341880    0.046875000 0.002504006
## 42 121_high  121   high 0.00000000    0.000000000 0.000000000
## 43  141_low  141    low 0.30102710    0.256465517 0.077203070
## 44  160_low  160    low 0.49533800    0.106162431 0.052586286
## 45   23_low   23    low 0.04941239    0.175000000 0.008647169
## 46 399_high  399   high 0.23103632    0.159663866 0.036888153
## 47  399_low  399    low 0.23698524    0.303921569 0.072024925
## 48   84_low   84    low 0.20834691    0.120035703 0.025009067
## 49  134_low  134    low 0.07470539    0.128205128 0.009577614
## 50  384_low  384    low 0.24621212    0.416666667 0.102588384
## 51  84_high   84   high 0.00000000    0.000000000 0.000000000
## 52 197_high  197   high 0.72916667    0.565217391 0.412137681
## 53  197_low  197    low 0.78125000    0.026666667 0.020833333
## 54   46_low   46    low 0.45454545    0.064814815 0.029461279
## 55   53_low   53    low 0.11363636    0.000000000 0.000000000
## 56 129_high  129   high 0.00000000    0.000000000 0.000000000
## 57  129_low  129    low 0.57849702    0.090425532 0.052310901
## 58   17_low   17    low 0.41666667    0.070422535 0.029342723
## 59   54_low   54    low 0.23674242    0.169706180 0.040176653
## 60   89_low   89    low 0.07352941    0.120000000 0.008823529
## 61 295_high  295   high 0.96590909    0.424640400 0.410164023
## 62  295_low  295    low 0.48413826    0.225877193 0.109355791
## 63   83_low   83    low 1.10294118    0.133858268 0.147637795
## 64  92_high   92   high 0.19943020    0.358333333 0.071462488
## 65   97_low   97    low 0.05208333    0.000000000 0.000000000
## 66  269_low  269    low 0.06410256    0.500000000 0.032051282
## 67   26_low   26    low 0.00000000    0.000000000 0.000000000
## 68   72_low   72    low 0.49242424    0.288888889 0.142255892
## 69  72_high   72   high 0.29166667    0.500000000 0.145833333
## 70  265_low  265    low 0.00000000    0.000000000 0.000000000

## 
##  Kruskal-Wallis rank sum test
## 
## data:  SDE by Height
## Kruskal-Wallis chi-squared = 8.7213, df = 1, p-value = 0.003145
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## Height       1  1.000  1.0005   18.15 6.43e-05 ***
## Residuals   68  3.748  0.0551                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Loading required package: carData
## Registered S3 methods overwritten by 'car':
##   method                          from
##   influence.merMod                lme4
##   cooks.distance.influence.merMod lme4
##   dfbeta.influence.merMod         lme4
##   dfbetas.influence.merMod        lme4
## 
## Attaching package: 'car'
## The following object is masked from 'package:boot':
## 
##     logit
## The following object is masked from 'package:psych':
## 
##     logit
## The following object is masked from 'package:purrr':
## 
##     some
## The following object is masked from 'package:dplyr':
## 
##     recode
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value    Pr(>F)    
## group  1  19.494 3.705e-05 ***
##       68                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Checking significance in differences of SDE

## Parsed with column specification:
## cols(
##   Species = col_character(),
##   `Scientific Name` = col_character(),
##   `Func. Group` = col_character(),
##   Sightings = col_double(),
##   Ingestions = col_double(),
##   Removals = col_double(),
##   Nibbles = col_double(),
##   `Avg. Vistitation Rate` = col_double(),
##   `Avg. Fruit Removal Rate` = col_double(),
##   SDE = col_double(),
##   Class = col_character()
## )

## file saved to Table3.png
## file saved to SPPtable.pdf
## Note that HTML color may not be displayed on PDF properly.
## [1] "FD"  "NFD"
## # A tibble: 2 x 4
##   `Func. Group` count  mean    sd
##   <fct>         <int> <dbl> <dbl>
## 1 FD                7 2.14  2.05 
## 2 NFD              13 0.446 0.454
## # A tibble: 20 x 11
##    Species `Scientific Nam… `Func. Group` Sightings Ingestions Removals Nibbles
##    <chr>   <chr>            <fct>             <dbl>      <dbl>    <dbl>   <dbl>
##  1 Centra… "Dasyprocta pun… NFD                 260          0       44      70
##  2 Brown … "Metachirus nud… NFD                  82          2        2       4
##  3 Baudó … "Penelope orton… FD                   20          2        4       2
##  4 Choco … "Ramphastos bre… FD                  272         99      142       3
##  5 Chestn… "Ramphastos amb… FD                  316        157      168       1
##  6 South … "Nasua nasua "   NFD                 458          2        0     175
##  7 Collar… "Pecari tajacu"  NFD                 136          4        0       0
##  8 Kinkaj… "Potos flavus"   NFD                  84          0        3      14
##  9 Oilbird "Steatornis car… FD                   73          0       36       0
## 10 Common… "Didelphis mars… NFD                 100          1        0      27
## 11 Lowlan… "Cuniculus paca" NFD                 416          0       31      73
## 12 Rufous… "Odontophorus e… NFD                 434          0        0       4
## 13 Rodent… ""               NFD                1380          0      197      45
## 14 Rufous… "Diplomys labil… NFD                   5          0        0       1
## 15 Southe… "Amazona farino… FD                   10          0        7       0
## 16 Squirr… ""               NFD                 675          0      249     109
## 17 Toucan… ""               FD                  284         75       85       0
## 18 Tome's… "Proechimys sem… NFD                 136          0       24       1
## 19 Long-w… "Cephalopterus … FD                  269         34       96       2
## 20 Brown … "Aramides wolfi" NFD                 127          0        0       7
## # … with 4 more variables: `Avg. Vistitation Rate` <dbl>, `Avg. Fruit Removal
## #   Rate` <dbl>, SDE <dbl>, Class <chr>

## 
##  One-way analysis of means (not assuming equal variances)
## 
## data:  SDE and Func_group
## F = 4.641, num df = 1.0000, denom df = 6.3171, p-value = 0.07238
## 
##  Kruskal-Wallis rank sum test
## 
## data:  SDE by Func_group
## Kruskal-Wallis chi-squared = 5.3009, df = 1, p-value = 0.02131
##             Df Sum Sq Mean Sq F value  Pr(>F)   
## Func_group   1  13.05  13.054   8.462 0.00936 **
## Residuals   18  27.77   1.543                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = SDE ~ Func_group, data = spptable)
## 
## $Func_group
##             diff       lwr        upr     p adj
## NFD-FD -1.693846 -2.917178 -0.4705144 0.0093621
##             Df Sum Sq Mean Sq F value Pr(>F)  
## Class        1   6.40   6.401   3.347 0.0839 .
## Residuals   18  34.42   1.912                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = SDE ~ Class, data = spptable)
## 
## $Class
##                  diff       lwr       upr     p adj
## Mammal-Bird -1.137172 -2.443006 0.1686627 0.0839294
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value  Pr(>F)  
## group  1  6.2905 0.02194 *
##       18                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value  Pr(>F)  
## group  1  5.2885 0.03365 *
##       18                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Visualization of ht vs focal 50

## `geom_smooth()` using formula 'y ~ x'

## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

Correlation at each band

effect of height focal against visitation

##    Tree Height Month Year           Species   utm1  utm2 ele Visitation
## 16   79   high    10 2016 Oenocarpus bataua 644127 38676 540        189
##    Richness M.Richness B.Richness T.Ingestions T.Removal T.Nibble
## 16        5          0          5           57       117        0
##    Avg.synch.neighbors Lek. DAP_CENSUS_1 ALTURA_CENSUS_1 NOTAS_CENSUS_1
## 16                  NA LEK1         29.5              21             NA
##    TIPO_DE_BOSQUE_COLLECTION DOSEL_CENSUS_1 CANOPY_DENS_CENSUS_1
## 16                Secundario             21                91.42
##    ARB_DAP10_CENSUS_1 ARB_DAP50_CENSUS_1 CERCROPIA_CENSUS_1 MICONIA_CENSUS_1
## 16                  9                  0                  0                0
##    JUV_CENSUS_1 JUV_DENS_CENSUS_1 PLANTULA_CENSUS_1 PLANTULA_DENS_CENSUS_1
## 16            4        0.05092958                41               2.088113
##    m.visitation b.visitation real.visitation real.m.visitation
## 16            0          189             189                 0
##    real.b.visitation real.richness real.m.richness real.b.richness  date
## 16               189             5               0               5 42644
##    focalmonth..50 focalmonth..100 focalmonth..150 focalmonth..200
## 16              2               3               6               9
##    focalmonth..250 focalmonth..300 focalmonth..350 focalmonth..400
## 16              10              12              15              16
##    focalmonth..450 focalmonth..500 TD  FD NRFM NFM NRFG NFG   start      end
## 16              16              16  0 189    1   d    1   e 10/5/16 10/16/16
##    days vis.rate n50 n100 n150 n200 n250 n300 n350 n400 n450 n500      Dates
## 16   11 17.18182   5   15   37   61   81  114  140  152  160  173 2016-10-01
##     FD.rate TD.rate
## 16 17.18182       0

Models 50 m band

Models using functional groups

Model Comparisons

Model 1: Original

## real.visitation ~ focalmonth..50 * Height + offset(lograte) + 
##     (1 + focalmonth..50 | Tree)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.0405307 0.1886986 0.2147905 0.8299306
focalmonth..50 0.5152201 0.2956979 1.7423871 0.0814407
Heightlow 0.7238617 0.0437088 16.5610200 0.0000000
focalmonth..50:Heightlow -0.2752461 0.0374950 -7.3408839 0.0000000 
## real.visitation ~ focalmonth..450 * Height + offset(lograte) + 
##     (1 + focalmonth..450 | Tree)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.2234468 0.3520308 0.6347365 0.5256003
focalmonth..450 -0.0047342 0.0302651 -0.1564254 0.8756977
Heightlow 0.4050716 0.0852282 4.7527900 0.0000020
focalmonth..450:Heightlow 0.0042980 0.0059529 0.7220041 0.4702920 
## Parsed with column specification:
## cols(
##   X1 = col_character(),
##   `z/tau value` = col_double(),
##   `±SE` = col_double(),
##   `p value` = col_double()
## )

## file saved to TableGLMM2.pdf

Model 2: Trees without any visits from Dispersers are removed

## real.visitation ~ focalmonth..50 * Height + offset(lograte) + 
##     (1 + focalmonth..50 | Tree)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.0415496 0.1880433 0.2209575 0.8251255
focalmonth..50 0.5152245 0.2960341 1.7404230 0.0817848
Heightlow 0.7235774 0.0436765 16.5667539 0.0000000
focalmonth..50:Heightlow -0.2750984 0.0374846 -7.3389674 0.0000000

Results are qualitatively similar.

Model 3: Original amount of trees, but fruiting neighborhood is now binary

## real.visitation ~ bin50 * Height + offset(lograte) + (1 + bin50 | 
##     Tree)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.0439227 0.1998483 0.219780 0.8260425
bin501 0.5713877 0.3322725 1.719636 0.0854987
Heightlow 0.6624992 0.0483892 13.691042 0.0000000
bin501:Heightlow -0.3084512 0.0692597 -4.453547 0.0000084

Again, binarizing is qualitatively similar.

Model 4: Original amount of trees, but fruiting neighborhood categorized into 0,1,2,3+

## real.visitation ~ bin50 * Height + offset(lograte) + (1 | Tree)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.1688387 0.1767227 0.9553876 0.3393817
bin50(1) 0.1033138 0.0840268 1.2295336 0.2188718
bin50(2) 0.0176622 0.1071663 0.1648108 0.8690929
bin50(3) -0.4130312 0.1485860 -2.7797440 0.0054402
Heightlow 0.6869025 0.0479722 14.3187591 0.0000000
bin50(1):Heightlow -0.2540545 0.0754133 -3.3688264 0.0007549
bin50(2):Heightlow -0.8947907 0.1114449 -8.0289979 0.0000000
bin50(3):Heightlow -0.2842984 0.1605031 -1.7712948 0.0765117

Model 5: Original data but tree with outlier in visitation in high cameras removed

## real.visitation ~ focalmonth..50 * Height + offset(lograte) + 
##     (1 + focalmonth..50 | Tree)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.0650785 0.1908849 0.3409307 0.7331558
focalmonth..50 0.3663191 0.3096886 1.1828626 0.2368636
Heightlow 0.6933769 0.0439534 15.7752735 0.0000000
focalmonth..50:Heightlow -0.0700859 0.0400875 -1.7483212 0.0804084

Focalmonth..50 and the interaction term are no longer significant.

Model 6: Trees without any visits from Dispersers are removed AND outlier in visitation in high cameras removed

## real.visitation ~ focalmonth..50 * Height + offset(lograte) + 
##     (1 + focalmonth..50 | Tree)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.0657147 0.1905568 0.3448563 0.7302024
focalmonth..50 0.3664713 0.3096947 1.1833307 0.2366781
Heightlow 0.6931354 0.0439257 15.7797227 0.0000000
focalmonth..50:Heightlow -0.0699615 0.0400800 -1.7455466 0.0808898

Results are qualitatively similar again when trees with 0 disperser visitation is removed

Checking SDE on the tree level

## file saved to euptcvm.pdf

Network Analysis

## specialisation asymmetry                       H2 
##                0.1895788                0.4342014
## weighted.cluster.coefficient.HL weighted.cluster.coefficient.LL 
##                       0.8235261                       0.9700709 
##                   generality.HL                vulnerability.LL 
##                      12.9410832                       3.5444669

## specialisation asymmetry                       H2 
##               0.09440097               0.35740596
## weighted.cluster.coefficient.HL weighted.cluster.coefficient.LL 
##                       0.7975048                       0.9144969 
##                   generality.HL                vulnerability.LL 
##                       6.0560145                       3.7946460

Correlation between Fruiting neighborhood (FN) at 50m and for FN at whole study plot

## 
##  Shapiro-Wilk normality test
## 
## data:  dummy$fn1400
## W = 0.93961, p-value = 4.615e-06
## 
##  Shapiro-Wilk normality test
## 
## data:  dummy$FN50
## W = 0.69195, p-value < 2.2e-16
Correlation between fruiting neighborhood at 50 m and entire plot.
statistic p.value kendall_score denominator var_kendall_score
0.1360697 0.0378435 1118 8216.378 289349.8

Model 1: total rate ~ FN50 + FNTotal + Camera height

Model 2: Fd rate ~ FN50 + FNTotal + Camera height

Model 3: Non Flying d rate ~ FN50 + FNTotal + Camera height

##  Family: poisson  ( log )
## Formula:          FD.V ~ FN50 + Height + fn1400 + offset(lograte) + (1 | Tree)
## Zero inflation:        ~FN50
## Data: dummy
## 
##      AIC      BIC   logLik deviance df.resid 
##       NA       NA       NA       NA      144 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  Tree   (Intercept) 2.068    1.438   
## Number of obs: 151, groups:  Tree, 47
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.71708    0.15653   -4.58 4.62e-06 ***
## FN50        -0.41439    0.10446   -3.97 7.27e-05 ***
## Heightlow   -1.97636    0.03695  -53.49  < 2e-16 ***
## fn1400       0.07405    0.01093    6.77 1.25e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Zero-inflation model:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -0.02337    0.07064  -0.331   0.7408  
## FN50         0.34464    0.17642   1.954   0.0508 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##  Family: poisson  ( log )
## Formula:          
## FD.V ~ FN50 * Heightlf + fn1400 + offset(lograte) + (1 | Tree)
## Zero inflation:        ~1
## Data: dummy
## 
##      AIC      BIC   logLik deviance df.resid 
##       NA       NA       NA       NA      144 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  Tree   (Intercept) 1.909    1.382   
## Number of obs: 151, groups:  Tree, 47
## 
## Conditional model:
##                   Estimate Std. Error z value Pr(>|z|)    
## (Intercept)       -3.16099    0.07589  -41.65   <2e-16 ***
## FN50              -2.21497         NA      NA       NA    
## Heightlfhigh       1.94215         NA      NA       NA    
## fn1400             0.06695         NA      NA       NA    
## FN50:Heightlfhigh  1.86121         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Zero-inflation model:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)  -0.0296         NA      NA       NA
  Visitation rate with random intercept Flying visitation rate with random intercept Non-Flying visitation rate with random intercept
Predictors Incidence Rate Ratios CI p Incidence Rate Ratios CI p Incidence Rate Ratios CI p
(Intercept) 0.61 0.45 – 0.82 0.001 0.49 0.36 – 0.66 <0.001 0.58 0.33 – 0.99 0.047
FN50 0.85 0.78 – 0.92 <0.001 0.66 0.54 – 0.81 <0.001 0.93 0.83 – 1.04 0.194
Height [low] 0.58 0.53 – 0.65 <0.001 0.14 0.13 – 0.15 <0.001 1.26 1.00 – 1.59 0.051
fn1400 1.04 1.03 – 1.05 <0.001 1.08 1.05 – 1.10 <0.001 1.00 0.98 – 1.02 0.882
Zero-Inflated Model
(Intercept) 0.36 0.23 – 0.56 <0.001 0.98 0.85 – 1.12 0.741 0.94 0.64 – 1.40 0.767
FN50 0.83 0.55 – 1.26 0.387 1.41 1.00 – 1.99 0.051 0.80 0.57 – 1.12 0.189
Random Effects
σ2 0.71 1.80 0.54
Ï„00 0.78 Tree 2.07 Tree 0.69 Tree
ICC 0.52 0.54 0.56
N 47 Tree 47 Tree 47 Tree
Observations 151 151 151
Marginal R2 / Conditional R2 0.094 / 0.568 0.257 / 0.655 0.014 / 0.565
Estimate Estimate Estimate
(Intercept) -0.4931816 -0.7170830 -0.5527095
FN50 -0.1651333 -0.4143911 -0.0726639
Heightlow -0.5404367 -1.9763615 0.2316179
fn1400 0.0399221 0.0740467 0.0016721

Model 3.5 Models

##  Family: poisson  ( log )
## Formula:          
## total.V ~ FN50 * fn1400 + Height + offset(lograte) + (1 | Tree)
## Zero inflation:           ~FN50
## Data: dummy
## 
##      AIC      BIC   logLik deviance df.resid 
##   1966.5   1990.7   -975.3   1950.5      143 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  Tree   (Intercept) 0.778    0.882   
## Number of obs: 151, groups:  Tree, 47
## 
## Conditional model:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -4.924e-01  1.867e-01  -2.638  0.00834 ** 
## FN50        -1.662e-01  1.570e-01  -1.059  0.28964    
## fn1400       3.990e-02  7.766e-03   5.138 2.78e-07 ***
## Heightlow   -5.404e-01  5.240e-02 -10.313  < 2e-16 ***
## FN50:fn1400  5.275e-05  7.173e-03   0.007  0.99413    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Zero-inflation model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.0242     0.2246  -4.560 5.12e-06 ***
## FN50         -0.1824     0.2141  -0.852    0.394    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##  Family: poisson  ( log )
## Formula:          FD.V ~ FN50 * fn1400 + Height + offset(lograte) + (1 | Tree)
## Zero inflation:        ~FN50
## Data: dummy
## 
##      AIC      BIC   logLik deviance df.resid 
##    967.2    991.3   -475.6    951.2      143 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  Tree   (Intercept) 1.55     1.245   
## Number of obs: 151, groups:  Tree, 47
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.80919    0.28804  -2.809  0.00497 ** 
## FN50        -1.02918    0.26078  -3.947 7.93e-05 ***
## fn1400       0.05025    0.01291   3.894 9.87e-05 ***
## Heightlow   -1.97516    0.22285  -8.863  < 2e-16 ***
## FN50:fn1400  0.03195    0.01158   2.760  0.00579 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Zero-inflation model:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)   0.1271     0.2412   0.527    0.598
## FN50          0.2714     0.2235   1.214    0.225

##  Family: poisson  ( log )
## Formula:          TD.V ~ FN50 * fn1400 + Height + offset(lograte) + (1 | Tree)
## Zero inflation:        ~FN50
## Data: dummy
## 
##      AIC      BIC   logLik deviance df.resid 
##    982.3   1006.5   -483.2    966.3      143 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  Tree   (Intercept) 0.7233   0.8505  
## Number of obs: 151, groups:  Tree, 47
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -0.75049    0.30000  -2.502   0.0124 *
## FN50         0.61528    0.30310   2.030   0.0424 *
## fn1400       0.01215    0.01217   0.998   0.3181  
## Heightlow    0.19858    0.11980   1.658   0.0974 .
## FN50:fn1400 -0.03165    0.01368  -2.313   0.0207 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Zero-inflation model:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.06295    0.20202  -0.312    0.755
## FN50        -0.21965    0.17130  -1.282    0.200
  Visitation rate with random intercept Visitation rate with random intercept AND SLOPE Flying visitation rate with random intercept
Predictors Incidence Rate Ratios CI p Incidence Rate Ratios CI p Incidence Rate Ratios CI p
(Intercept) 0.61 0.42 – 0.88 0.008 0.45 0.25 – 0.78 0.005 0.47 0.26 – 0.85 0.012
FN50 0.85 0.62 – 1.15 0.290 0.36 0.21 – 0.60 <0.001 1.85 1.02 – 3.35 0.042
fn1400 1.04 1.02 – 1.06 <0.001 1.05 1.03 – 1.08 <0.001 1.01 0.99 – 1.04 0.318
Height [low] 0.58 0.53 – 0.65 <0.001 0.14 0.09 – 0.21 <0.001 1.22 0.96 – 1.54 0.097
FN50 * fn1400 1.00 0.99 – 1.01 0.994 1.03 1.01 – 1.06 0.006 0.97 0.94 – 1.00 0.021
Zero-Inflated Model
(Intercept) 0.36 0.23 – 0.56 <0.001 1.14 0.71 – 1.82 0.598 0.94 0.63 – 1.40 0.755
FN50 0.83 0.55 – 1.27 0.394 1.31 0.85 – 2.03 0.225 0.80 0.57 – 1.12 0.200
Random Effects
σ2 0.71 1.70 0.55
Ï„00 0.78 Tree 1.55 Tree 0.72 Tree
ICC 0.52 0.48 0.57
N 47 Tree 47 Tree 47 Tree
Observations 151 151 151
Marginal R2 / Conditional R2 0.094 / 0.567 0.287 / 0.628 0.025 / 0.576
AIC 1966.533 967.186 982.329
Estimate Estimate Estimate
(Intercept) -0.4924035 -0.8091924 -0.7504906
FN50 -0.1662483 -1.0291770 0.6152846
fn1400 0.0398962 0.0502546 0.0121530
Heightlow -0.5404348 -1.9751555 0.1985775
FN50:fn1400 0.0000527 0.0319515 -0.0316486

Model 4: All models including crop size but no interaction terms

##  Family: poisson  ( log )
## Formula:          
## total.V ~ FN50 + Height + fn1400 + cropsize + offset(lograte) +      (1 | Tree)
## Zero inflation:           ~FN50
## Data: dummy
## 
##      AIC      BIC   logLik deviance df.resid 
##   1901.7   1934.9   -939.8   1879.7      140 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  Tree   (Intercept) 0.7214   0.8494  
## Number of obs: 151, groups:  Tree, 47
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.563966   0.236021  -2.389  0.01687 *  
## FN50        -0.120211   0.043809  -2.744  0.00607 ** 
## Heightlow   -0.595176   0.054561 -10.908  < 2e-16 ***
## fn1400       0.036728   0.008522   4.310 1.63e-05 ***
## cropsize.L   0.166032   0.254977   0.651  0.51494    
## cropsize.Q  -0.442950   0.222020  -1.995  0.04603 *  
## cropsize.C   0.014846   0.183665   0.081  0.93557    
## cropsize^4   0.512372   0.111204   4.608 4.08e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Zero-inflation model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.0026     0.2250  -4.455 8.38e-06 ***
## FN50         -0.1581     0.2043  -0.774    0.439    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Data: dummy
## Models:
## m2: total.V ~ FN50 + Height + fn1400 + (1 | Tree) + offset(lograte), zi=~FN50, disp=~1
## model.1RIcrop: total.V ~ FN50 + Height + fn1400 + cropsize + offset(lograte) + , zi=~FN50, disp=~1
## model.1RIcrop:     (1 | Tree), zi=~FN50, disp=~1
##               Df    AIC    BIC  logLik deviance  Chisq Chi Df Pr(>Chisq)    
## m2             7 1964.5 1985.7 -975.27   1950.5                             
## model.1RIcrop 11 1901.7 1934.9 -939.85   1879.7 70.837      4  1.511e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## # R2 for Mixed Models
## 
##   Conditional R2: 0.562
##      Marginal R2: 0.127
##   FN50 Height fn1400 cropsize emmean    SE  df lower.CL upper.CL
##  0.662 high     17.3 a          2.17 0.430 140    1.322     3.02
##  0.662 low      17.3 a          1.58 0.426 140    0.735     2.42
##  0.662 high     17.3 b          2.29 0.240 140    1.814     2.76
##  0.662 low      17.3 b          1.69 0.244 140    1.209     2.18
##  0.662 high     17.3 c          3.06 0.173 140    2.720     3.40
##  0.662 low      17.3 c          2.47 0.169 140    2.132     2.80
##  0.662 high     17.3 d          2.37 0.172 140    2.034     2.71
##  0.662 low      17.3 d          1.78 0.171 140    1.440     2.12
##  0.662 high     17.3 e          2.39 0.179 140    2.038     2.74
##  0.662 low      17.3 e          1.80 0.181 140    1.438     2.16
## 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95

##  Family: poisson  ( log )
## Formula:          FD.V ~ FN50 + Height + fn1400 + cropsize + offset(lograte) +  
##     (1 | Tree)
## Zero inflation:        ~FN50
## Data: dummy
## 
##      AIC      BIC   logLik deviance df.resid 
##       NA       NA       NA       NA      140 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  Tree   (Intercept) 2.005    1.416   
## Number of obs: 151, groups:  Tree, 47
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.142008   0.100966  -11.31  < 2e-16 ***
## FN50        -0.351001   0.078046   -4.50 6.88e-06 ***
## Heightlow   -2.389815   0.194816  -12.27  < 2e-16 ***
## fn1400       0.027412   0.003297    8.31  < 2e-16 ***
## cropsize.L   3.122236   0.264788   11.79  < 2e-16 ***
## cropsize.Q  -2.426704   0.030621  -79.25  < 2e-16 ***
## cropsize.C   2.091143   0.143001   14.62  < 2e-16 ***
## cropsize^4  -0.539855   0.163815   -3.30 0.000982 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Zero-inflation model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -0.1757     0.2565  -0.685    0.493    
## FN50          0.4473     0.1063   4.206  2.6e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Not enough model terms in the zero_inflated part of the model to check for multicollinearity.
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

## Data: dummy
## Models:
## model.2crop: FD.V ~ FN50 + Height + fn1400 + cropsize + offset(lograte) + , zi=~FN50, disp=~1
## model.2crop:     (1 | Tree), zi=~FN50, disp=~1
## m2: FD.V ~ FN50 + Height + fn1400 + cropsize + (1 | Tree) + offset(lograte), zi=~FN50, disp=~1
##             Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## model.2crop 11                                                
## m2          11                                    0

## Data: dummy
## Models:
## model.3crop: TD.V ~ FN50 + Height + fn1400 + cropsize + offset(lograte) + , zi=~FN50, disp=~1
## model.3crop:     (1 | Tree), zi=~FN50, disp=~1
## m2: TD.V ~ FN50 + Height + fn1400 + cropsize + (1 | Tree) + offset(lograte), zi=~FN50, disp=~1
##             Df   AIC    BIC  logLik deviance Chisq Chi Df Pr(>Chisq)
## model.3crop 11 930.3 963.49 -454.15    908.3                        
## m2          11 930.3 963.49 -454.15    908.3     0      0          1
  total.V FD.V TD.V
Predictors Incidence Rate Ratios CI p Incidence Rate Ratios CI p Incidence Rate Ratios CI p
(Intercept) 0.57 0.36 – 0.90 0.017 0.32 0.26 – 0.39 <0.001 0.30 0.15 – 0.62 0.001
FN50 0.89 0.81 – 0.97 0.006 0.70 0.60 – 0.82 <0.001 0.88 0.78 – 0.98 0.022
Height [low] 0.55 0.50 – 0.61 <0.001 0.09 0.06 – 0.13 <0.001 1.40 1.09 – 1.80 0.009
fn1400 1.04 1.02 – 1.05 <0.001 1.03 1.02 – 1.03 <0.001 1.00 0.98 – 1.03 0.792
cropsize.L 1.18 0.72 – 1.95 0.515 22.70 13.51 – 38.14 <0.001 2.65 1.35 – 5.20 0.005
cropsize.Q 0.64 0.42 – 0.99 0.046 0.09 0.08 – 0.09 <0.001 0.78 0.43 – 1.41 0.403
cropsize.C 1.01 0.71 – 1.45 0.936 8.09 6.12 – 10.71 <0.001 0.52 0.19 – 1.40 0.194
cropsize^4 1.67 1.34 – 2.08 <0.001 0.58 0.42 – 0.80 0.001 3.50 1.72 – 7.12 0.001
Zero-Inflated Model
(Intercept) 0.37 0.24 – 0.57 <0.001 0.84 0.51 – 1.39 0.493 0.87 0.52 – 1.47 0.611
FN50 0.85 0.57 – 1.27 0.439 1.56 1.27 – 1.93 <0.001 0.82 0.56 – 1.18 0.280
Random Effects
σ2 0.73 2.05 0.51
Ï„00 0.72 Tree 2.00 Tree 0.63 Tree
ICC 0.50 0.49 0.55
N 47 Tree 47 Tree 47 Tree
Observations 151 151 151
Marginal R2 / Conditional R2 0.127 / 0.562 0.321 / 0.656 0.197 / 0.638

Model 5: cropsize again but binarized with low being below 500

##  Family: poisson  ( log )
## Formula:          
## total.V ~ FN50 + Height + fn1400 + csizeb + offset(lograte) +      (1 | Tree)
## Zero inflation:           ~FN50
## Data: dummy
## 
##      AIC      BIC   logLik deviance df.resid 
##   1920.2   1944.3   -952.1   1904.2      143 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  Tree   (Intercept) 0.7294   0.854   
## Number of obs: 151, groups:  Tree, 47
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.596084   0.207320  -2.875  0.00404 ** 
## FN50        -0.120066   0.044043  -2.726  0.00641 ** 
## Heightlow   -0.569945   0.053448 -10.664  < 2e-16 ***
## fn1400       0.035523   0.007259   4.893 9.91e-07 ***
## csizebLow    0.508652   0.073704   6.901 5.15e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Zero-inflation model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.0017     0.2234  -4.484 7.32e-06 ***
## FN50         -0.1551     0.2037  -0.761    0.447    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##  Family: poisson  ( log )
## Formula:          
## FD.V ~ FN50 + Height + fn1400 + csizeb + offset(lograte) + (1 |      Tree)
## Zero inflation:        ~FN50
## Data: dummy
## 
##      AIC      BIC   logLik deviance df.resid 
##    972.6    996.7   -478.3    956.6      143 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  Tree   (Intercept) 1.563    1.25    
## Number of obs: 151, groups:  Tree, 47
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.07453    0.28910  -3.717 0.000202 ***
## FN50        -0.32242    0.08240  -3.913 9.12e-05 ***
## Heightlow   -2.24234    0.26167  -8.569  < 2e-16 ***
## fn1400       0.06250    0.01169   5.344 9.08e-08 ***
## csizebLow    0.19925    0.12580   1.584 0.113234    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Zero-inflation model:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)  0.06932    0.25576   0.271    0.786
## FN50         0.28032    0.22452   1.248    0.212
##  Family: tweedie  ( log )
## Formula:          
## TD.V ~ FN50 + Height + fn1400 + csizeb + offset(lograte) + (1 |      Tree)
## Zero inflation:        ~FN50
## Data: dummy
## 
##      AIC      BIC   logLik deviance df.resid 
##    748.0    778.2   -364.0    728.0      141 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  Tree   (Intercept) 0.4007   0.633   
## Number of obs: 151, groups:  Tree, 47
## 
## Overdispersion parameter for tweedie family (): 4.89 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -3.22212    0.48325  -6.668 2.60e-11 ***
## FN50        -0.03436    0.12926  -0.266   0.7904    
## Heightlow    1.76597    0.27139   6.507 7.66e-11 ***
## fn1400       0.04854    0.02040   2.379   0.0174 *  
## csizebLow    0.47916    0.25973   1.845   0.0651 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Zero-inflation model:
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept)   -19.320   5643.465  -0.003    0.997
## FN50           -2.112  14674.518   0.000    1.000

Outcome table

X FN50 FNTOTAL Height FN50.x.FN1400
Total Visitation NS .004 / p=0 -0.054 / p=0 NS
Flying visitiation -1.02 / p=0 0.05 / p=0 -.196 / p=0 0.03 / p= 0.006
non-flying visitation .61 / p=0.0424 NS NS -0.03 / P=.0207 
##  Family: poisson  ( log )
## Formula:          
## total.V ~ FN50 * fn1400 + Height + offset(lograte) + (1 | Tree)
## Zero inflation:           ~FN50
## Data: dummy
## 
##      AIC      BIC   logLik deviance df.resid 
##   1966.5   1990.7   -975.3   1950.5      143 
## 
## Random effects:
## 
## Conditional model:
##  Groups Name        Variance Std.Dev.
##  Tree   (Intercept) 0.778    0.882   
## Number of obs: 151, groups:  Tree, 47
## 
## Conditional model:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -4.924e-01  1.867e-01  -2.638  0.00834 ** 
## FN50        -1.662e-01  1.570e-01  -1.059  0.28964    
## fn1400       3.990e-02  7.766e-03   5.138 2.78e-07 ***
## Heightlow   -5.404e-01  5.240e-02 -10.313  < 2e-16 ***
## FN50:fn1400  5.275e-05  7.173e-03   0.007  0.99413    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Zero-inflation model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.0242     0.2246  -4.560 5.12e-06 ***
## FN50         -0.1824     0.2141  -0.852    0.394    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## NOTE: fn1400 is not a high-order term in the model

## NOTE: FN50 is not a high-order term in the model

## NOTE: fn1400 is not a high-order term in the model

## NOTE: FN50 is not a high-order term in the model

## NOTE: fn1400 is not a high-order term in the model

## NOTE: FN50 is not a high-order term in the model